CalFishTrack

Central Valley Enhanced

Acoustic Tagging Project

6-Year Study San Joaquin River Steelhead - March Releases

2021-2022 Season (PROVISIONAL DATA)

---

1. Project Status

---

Telemetry Study Template for this study can be found here

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

library(knitr)
library(kableExtra)
library(lubridate)
library(data.table)
library(ggplot2)
library(RMark)
library(scales)
library(viridis)
library(forcats)
library(reshape2)
library(png)
library(dataRetrieval)
library(rerddap)
library(plotly)
library(dplyr)

##################################################################################################################
#### ASSIGN STUDY ID IN THIS NEXT LINE OF CODE ####
study <- "SJ_Steelhead_2022"
##################################################################################################################

detects_study <- fread("study_detections_archive.csv", stringsAsFactors = F, colClasses = c(DateTime_PST = "character", RelDT = "character"))
detects_study <- as.data.frame(detects_study[detects_study$Study_ID == study,])
detects_study$DateTime_PST <- as.POSIXct(detects_study$DateTime_PST, format = "%Y-%m-%d %H:%M:%S", tz="Etc/GMT+8")
detects_study$release_time <- as.POSIXct(detects_study$RelDT, format = "%Y-%m-%d %H:%M:%S", tz="Etc/GMT+8")
detects_study <- detects_study %>% filter(release_time < as.POSIXct("2022-04-01 00:00:00")) # get only March release

colnames(detects_study)[which(colnames(detects_study) == "Weight")] <- "weight"
colnames(detects_study)[which(colnames(detects_study) == "Length")] <- "length"
colnames(detects_study)[which(colnames(detects_study) == "Rel_rkm")] <- "release_rkm"
colnames(detects_study)[which(colnames(detects_study) == "Rel_loc")] <- "release_location"
colnames(detects_study)[which(colnames(detects_study) == "rkm")] <- "river_km"

latest <- read.csv("latest_download.csv", stringsAsFactors = F)$x

##################################################################################################################
#### TO RUN THE FOLLOWING CODE CHUNKS FROM HERE ON DOWN USING R ERDDAP, UN-COMMENT THESE NEXT 9 LINES OF CODE ####
##################################################################################################################
# cache_delete_all()
# query=paste('&',"Study_ID",'="',study,'"',sep = '')
# datafile=URLencode(paste("https://oceanview.pfeg.noaa.gov/erddap/tabledap/","FEDcalFishTrack",".csv?",query,sep = ''))
# options(url.method = "libcurl", download.file.method = "libcurl", timeout = 180)
# detects_study <- data.frame(read.csv(datafile,row.names = NULL, stringsAsFactors = F))
# detects_study <- detects_study[-1,]
# detects_study$DateTime_PST <- as.POSIXct(detects_study$local_time, format = "%Y-%m-%d %H:%M:%S", "Etc/GMT+8")
# detects_study$release_time <- as.POSIXct(detects_study$release_time, format = "%Y-%m-%d %H:%M:%S", "Etc/GMT+8")
# detects_study$river_km <- as.numeric(detects_study$river_km)
##################################################################################################################


if (nrow(detects_study) == 0){
  cat("Study has not yet begun\n  ")
}else{
  
  if (min(detects_study$release_time) > Sys.time()){
    cat("Study has not yet begun, below data is a placeholder:\n  ")
  }
  if (min(detects_study$release_time) < Sys.time()){
    cat(paste("Study began on ", min(detects_study$release_time), ", see tagging details below:", sep = ""))
  }
  
  ########################################################################
  #### ASSIGN RELEASE GROUPS HERE ####
  #######################################################################
  detects_study$Release <- detects_study$release_location
  # detects_study[detects_study$release_time > as.POSIXct("2022-03-01 12:00:00"), "Release"] <- "Week 2"
  #detects_study[detects_study$release_time > as.POSIXct("2021-01-30 14:31:00"), "Release"] <- "Release 3"
  #######################################################################
  
  study_tagcodes <- unique(detects_study[,c("TagCode", "release_time", "weight", "length", "release_rkm", "release_location", "Release")])

  
  release_stats <- aggregate(list(First_release_time = study_tagcodes$release_time),
                             by= list(Release = study_tagcodes$Release),
                             FUN = min)
  release_stats <- merge(release_stats,
                         aggregate(list(Last_release_time = study_tagcodes$release_time),
                                   by= list(Release = study_tagcodes$Release),
                                   FUN = max),
                         by = c("Release"))
  
  
  release_stats <- merge(release_stats, aggregate(list(Number_fish_released =
                                                         study_tagcodes$TagCode),
                                                  by= list(Release = study_tagcodes$Release),
                                                  FUN = function(x) {length(unique(x))}),
                         by = c("Release"))
  
  release_stats <- merge(release_stats,
                         aggregate(list(Release_location = study_tagcodes$release_location),
                                   by= list(Release = study_tagcodes$Release),
                                   FUN = function(x) {head(x,1)}),
                         by = c("Release"))
  release_stats <- merge(release_stats,
                         aggregate(list(Release_rkm = study_tagcodes$release_rkm),
                                   by= list(Release = study_tagcodes$Release),
                                   FUN = function(x) {head(x,1)}),
                         by = c("Release"))
  release_stats <- merge(release_stats,
                         aggregate(list(Mean_length = as.numeric(study_tagcodes$length)),
                                   by= list(Release = study_tagcodes$Release),
                                   FUN = mean, na.rm = T),
                         by = c("Release"))
  release_stats <- merge(release_stats,
                         aggregate(list(Mean_weight = as.numeric(study_tagcodes$weight)),
                                   by= list(Release = study_tagcodes$Release),
                                   FUN = mean, na.rm = T),
                         by = c("Release"))
  

  release_stats[,c("Mean_length", "Mean_weight")] <- round(release_stats[,c("Mean_length", "Mean_weight")],1)
  
  release_stats$First_release_time <- format(release_stats$First_release_time, tz = "Etc/GMT+8")
  
  release_stats$Last_release_time <- format(release_stats$Last_release_time, tz = "Etc/GMT+8")
  
  release_stats <- release_stats[order(release_stats$First_release_time),]
  
  kable(release_stats, format = "html", row.names = F) %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left")

}                       

Study began on 2022-03-15 08:05:00, see tagging details below:

Release	First_release_time	Last_release_time	Number_fish_released	Release_location	Release_rkm	Mean_length	Mean_weight
Head_of_Old_River	2022-03-15 08:05:00	2022-03-18 08:42:00	144	Head_of_Old_River	156.0	252.0	160.5
Durham_Ferry	2022-03-15 10:00:00	2022-03-18 08:30:00	248	Durham_Ferry	180.0	254.7	164.7
Stockton	2022-03-15 12:43:00	2022-03-18 12:59:00	100	Stockton	135.5	255.7	167.1

---

2. Real-time Fish Detections

---

Study is complete, all tags are no longer active as of 2022-07-14. All times in Pacific Standard Time.

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

## THIS CODE CHUNK WILL NOT WORK IF USING ONLY ERDDAP DATA, REQUIRES ACCESS TO LOCAL FILES
if (nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
  "No detections yet"
} else {
  arrivals <- aggregate(list(DateTime_PST = detects_study$DateTime_PST), by = list(general_location = detects_study$general_location, TagCode = detects_study$TagCode), FUN = min)
    
  beacon_by_day <- fread("beacon_by_day.csv", stringsAsFactors = F)
  beacon_by_day$day <- as.Date(beacon_by_day$day)
  
  gen_locs <- read.csv("realtime_locs.csv", stringsAsFactors = F)
  
  arrivals$day <- as.Date(format(arrivals$DateTime_PST, "%Y-%m-%d", tz = "Etc/GMT+8"))
  
  arrivals_per_day <- aggregate(list(New_arrivals = arrivals$TagCode), by = list(day = arrivals$day, general_location = arrivals$general_location), length)
  arrivals_per_day$day <- as.Date(arrivals_per_day$day)

  ## Now subset to only look at data for the correct beacon for that day
  beacon_by_day <- as.data.frame(beacon_by_day[which(beacon_by_day$TagCode == beacon_by_day$beacon),])
  
  endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")), max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life)*1.5)))
  ## Now only keep beacon by day for days since fish were released
  beacon_by_day <- beacon_by_day[beacon_by_day$day >= as.Date(min(study_tagcodes$release_time)) & beacon_by_day$day <= endtime,]  
  
  beacon_by_day <- merge(beacon_by_day, gen_locs[,c("location", "general_location","rkm")], by = "location", all.x = T)

  arrivals_per_day <- merge(unique(beacon_by_day[,c("general_location", "day", "rkm")]), arrivals_per_day, all.x = T, by = c("general_location", "day"))
  
  arrivals_per_day$day <- factor(arrivals_per_day$day)
  
  ## Remove bench test and other NA locations
  arrivals_per_day <- arrivals_per_day[!arrivals_per_day$general_location == "Bench_test",]
  arrivals_per_day <- arrivals_per_day[is.na(arrivals_per_day$general_location) == F,]

  ## Remove sites that were not operation the whole time
  #### FOR THE SEASONAL SURVIVAL PAGE, KEEP ALL SITES SINCE PEOPLE WANT TO SEE DETECTIONS OF LATER FISH AT NEWLY DEPLOYED SPOTS##
  gen_locs_days_in_oper <- aggregate(list(days_in_oper = arrivals_per_day$day), by = list(general_location = arrivals_per_day$general_location), FUN = length)
  #gen_locs_days_in_oper <- gen_locs_days_in_oper[gen_locs_days_in_oper$days_in_oper == max(gen_locs_days_in_oper$days_in_oper),]
  
  arrivals_per_day_in_oper <- arrivals_per_day[arrivals_per_day$general_location %in% gen_locs_days_in_oper$general_location,]
  fish_per_site <- aggregate(list(fish_count = arrivals_per_day_in_oper$New_arrivals), by = list(general_location = arrivals_per_day_in_oper$general_location), FUN = sum, na.rm = T)
  
  active_gen_locs <- gen_locs[is.na(gen_locs$stop),]
  active_gen_locs <- active_gen_locs[active_gen_locs$general_location %in% fish_per_site$general_location,]
  ## estimate mean lat and lons for each genloc
  gen_locs_mean_coords <- aggregate(list(latitude = active_gen_locs$latitude), by = list(general_location = active_gen_locs$general_location), FUN = mean)
  gen_locs_mean_coords <- merge(gen_locs_mean_coords, aggregate(list(longitude = active_gen_locs$longitude), by = list(general_location = active_gen_locs$general_location), FUN = mean))
  
  fish_per_site <- merge(fish_per_site, gen_locs_mean_coords)
  
  library(leaflet)
  library(maps)
  library(htmlwidgets)
  library(leaflet.extras)

  icons <- awesomeIcons(iconColor = "lightblue",
                      # library = "ion",
                      text = fish_per_site$fish_count)
  
  leaflet(data = fish_per_site) %>%
      # setView(-72.14600, 43.82977, zoom = 8) %>% 
      addProviderTiles("Esri.WorldStreetMap", group = "Map") %>%
      addProviderTiles("Esri.WorldImagery", group = "Satellite") %>% 
      addProviderTiles("Esri.WorldShadedRelief", group = "Relief") %>%
      # Marker data are from the sites data frame. We need the ~ symbols
      # to indicate the columns of the data frame.
      addMarkers(~longitude, ~latitude, label = ~fish_count, group = "Receiver Sites", popup = ~general_location, labelOptions = labelOptions(noHide = T, textsize = "15px")) %>% 
      # addAwesomeMarkers(~longitude, ~latitude, icon = icons, labelOptions(textsize = "15px")) %>%
      addScaleBar(position = "bottomleft") %>%
      addLayersControl(
          baseGroups = c("Street Map", "Satellite", "Relief"),
          options = layersControlOptions(collapsed = FALSE))
}

2.1 Map of unique fish detections at operational realtime detection locations

library(dplyr)
library(cder)
try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

recv_locs <- read.csv('realtime_locs.csv')

detects_omr_cor <- detects_study[detects_study$general_location %in% c("Old_River_Quimby", "Holland_Cut_Quimby", "Old River", "MiddleRiver", "Clifton_Court_US_Radial_Gates", "SWP_radial_gates_DS", "SWP_radial_gates_US", "CVP_Trash_Rack_1", "CVP_Tank", "SWP_intake", "Clifton_Court_Intake_Canal"),]

if (nrow(detects_omr_cor)>0) {
  # Save the last detection at each location
  detects_omr_cor <- merge(detects_omr_cor,aggregate(list(last_detect = detects_omr_cor$DateTime_PST), by = list(TagCode= detects_omr_cor$TagCode, general_location = detects_omr_cor$general_location), FUN = max))
  
  # Convert last detection to day
  detects_omr_cor$Day <- as.Date(detects_omr_cor$last_detect, "Etc/GMT+8")
  
  starttime <- as.Date(min(detects_omr_cor$release_time), "Etc/GMT+8")
  ## Endtime should be either now or end of predicted tag life, whichever comes first
  endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")),
             max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))
  
  daterange <- data.frame(Date = seq.Date(from = starttime, to = endtime, by = "day"))
  
  rels <- unique(study_tagcodes$Release)
  rel_num <- length(rels)
  rels_no_detects <- as.character(rels[!(rels %in% unique(detects_omr_cor$Release))])
  
  tagcount <- aggregate(list(unique_tags = detects_omr_cor$TagCode), 
                        by = list(Date = detects_omr_cor$Day, Location = detects_omr_cor$general_location), 
                        FUN = function(x){length(unique(x))})
  tagcount$Location <- factor(tagcount$Location, levels = c("Old_River_Quimby", "Holland_Cut_Quimby", "Old River",
                                                            "MiddleRiver", "Clifton_Court_US_Radial_Gates",
                                                            "SWP_radial_gates_DS", "SWP_radial_gates_US",
                                                            "CVP_Trash_Rack_1", "CVP_Tank", "SWP_intake",
                                                            "Clifton_Court_Intake_Canal"))
  tagcount1 <- reshape2::dcast(tagcount, Date ~ Location, drop = FALSE)
  daterange1 <- merge(daterange, tagcount1, all.x=T)
  
  daterange1[is.na(daterange1)] <- 0
  daterange2 <- daterange1
  rownames(daterange2) <- daterange2$Date
  
  par(mar=c(6, 5, 2, 5) + 0.1)
 
  daterange3 <- melt(daterange2, id.vars = "Date", variable.name = ".", )
  
  # Add latitude to daterange df
  locs <- data.frame(general_location = unique(daterange3$.)) %>% 
          left_join(., recv_locs, by = 'general_location') %>% filter(is.na(stop) | stop > min(detects_omr_cor$RelDT)) %>% 
          select(location, general_location, latitude) %>% group_by(general_location) %>% 
          summarise(latitude = mean(latitude))
  locs <- locs[order(locs$latitude, decreasing = FALSE),]
  locs$loc_num <- seq.int(nrow(locs))
  daterange4 <- daterange3 %>% left_join(., locs, by = c("." = "general_location"))

  # Get flow data from CDEC
  BTC_flow <- cdec_query("OMR", "20", "H", starttime, endtime)

  # Fish movement
  move_df <- detects_omr_cor %>%
             distinct(., TagCode, last_detect, general_location,
                      .keep_all = TRUE) # find last detection and remove duplicate value for that column
  dup_codes <- move_df$TagCode[which(duplicated(move_df$TagCode))] # then get the tag codes that were detected at multiple locations
  move_df <- move_df[(move_df$TagCode %in% dup_codes),] # save only the fish that were detected at multiple locations
  move_df$Day <- as.Date(move_df$last_detect, "Etc/GMT+8")
  new_move_df <- NULL
  for(i in 1:length(unique(move_df$TagCode))){
    tmp_code    <- unique(move_df$TagCode)[i]
    tmp_move_df <- move_df %>% filter(TagCode == tmp_code) # subset data
    tmp_move_df <- tmp_move_df[order(tmp_move_df$last_detect, decreasing = FALSE),] # order data
    for(j in 1:(length(tmp_move_df$TagCode) - 1)){
      tmp_new_move_df <- data.frame(TagCode = tmp_code, location1 = tmp_move_df$general_location[j],
                                   day1 = tmp_move_df$Day[j], location2 = tmp_move_df$general_location[j+1],
                                   day2 = tmp_move_df$Day[j+1])
      tmp_new_move_df$loc_num1 <- locs$loc_num[which(locs$general_location == tmp_new_move_df$location1)]
      tmp_new_move_df$loc_num2 <- locs$loc_num[which(locs$general_location == tmp_new_move_df$location2)]
      new_move_df <- rbind(new_move_df, tmp_new_move_df)
    }
  }

  fig1 <- plot_ly(data = daterange4, type = 'scatter', mode = "markers", 
                  marker = list(color = ~ value, size = ~value*5, 
                                colorbar = list(title = "Num. of arrivals", len = 0.35, x = 1.06, y = 0.73), 
                                colorscale = 'Viridis', line = list(width = 0)),
                   hoverinfo = 'text', text = ~paste('Date:', Date,'<br># of arrivals:', value),
                   x = ~Date, y = ~loc_num) %>%
          add_annotations(x = new_move_df$day2, y = new_move_df$loc_num2, axref="x", ayref="y", text="", showarrow=TRUE,
                          ax = new_move_df$day1, ay = new_move_df$loc_num1, arrowcolor = 'darkgrey',
                          opacity = 0.5, standoff = 5, startstandoff = 5) %>%
          layout(xaxis = list(range = c(min(daterange$Date) - 1, max(daterange$Date) + 1), showgrid = FALSE, showline = TRUE),
                 yaxis = list(range = c(min(locs$loc_num) - 1, max(locs$loc_num) + 1), showline = TRUE,
                 title = "", ticktext = locs$general_location, tickvals = locs$loc_num), showlegend = FALSE) %>% 
          add_annotations(text = sprintf("<b>North<b>"), xref = 'paper', yref = 'paper', x = -0.13, xanchor = 'left',
                          y = 1.05, yanchor = 'top', showarrow = FALSE, font = list(size = 20)) %>%
          add_annotations(text = sprintf("<b>South<b>"), xref = 'paper', yref = 'paper', x = -0.13, xanchor = 'left',
                          y = 0.04, yanchor = 'top', showarrow = FALSE, font = list(size = 20))
  fig2 <- plot_ly(data = BTC_flow, type = 'scatter', mode = 'lines') %>%
          add_trace(x = ~DateTime, y = ~Value) %>%
          layout(xaxis = list(range = c(min(daterange$Date) - 1, max(daterange$Date) + 1), showgrid = FALSE, showline = TRUE),
                 yaxis = list(title = "Flow (cfs) at OMR"))
  subplot(fig1, fig2, nrows = 2, margin = 0.04, heights = c(0.7, 0.3), titleY = TRUE) 

}else{
  plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
  text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
}

2.2 (BETA TEST) Detections in the Old and Middle rivers (OMR) for duration of tag life (top) and flow at OMR (bottom). Arrows indicate fish movement.

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

detects_benicia <- detects_study[detects_study$general_location %in% c("Benicia_west", "Benicia_east"),]

if (nrow(detects_benicia)>0) {
  detects_benicia <- merge(detects_benicia,aggregate(list(first_detect = detects_benicia$DateTime_PST), by = list(TagCode= detects_benicia$TagCode), FUN = min))
  
  detects_benicia$Day <- as.Date(detects_benicia$first_detect, "Etc/GMT+8")
  
  starttime <- as.Date(min(detects_benicia$release_time), "Etc/GMT+8")
  ## Endtime should be either now or end of predicted tag life, whichever comes first
  endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")), max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life))))
  #wlk_flow <- cdec_query("COL", "20", "H", starttime, endtime+1)
  #wlk_flow$datetime <- as.Date(wlk_flow$datetime)
  #wlk_flow_day <- aggregate(list(parameter_value = wlk_flow$parameter_value), by = list(Day = wlk_flow$datetime), FUN = mean, na.rm = T)
  
  daterange <- data.frame(Day = seq.Date(from = starttime, to = endtime, by = "day"))
  
  rels <- unique(study_tagcodes$Release)
  rel_num <- length(rels)
  rels_no_detects <- as.character(rels[!(rels %in% unique(detects_benicia$Release))])
  
  tagcount <- aggregate(list(unique_tags = detects_benicia$TagCode), by = list(Day = detects_benicia$Day, Release = detects_benicia$Release ), FUN = function(x){length(unique(x))})
  tagcount1 <- reshape2::dcast(tagcount, Day ~ Release)
                    
  daterange1 <- merge(daterange, tagcount1, all.x=T)
  daterange1[is.na(daterange1)] <- 0
  
  if(length(rels_no_detects)>0){
    for(i in rels_no_detects){
      daterange1 <- cbind(daterange1, x=NA)
      names(daterange1)[names(daterange1) == 'x'] <- paste(i)
    }
  }
  
  ## reorder columns in alphabetical order so its coloring in barplots is consistent
  daterange1 <- daterange1[,order(colnames(daterange1))]
    
  #daterange2 <- merge(daterange1, wlk_flow_day, by = "Day", all.x = T)
  daterange2 <- daterange1
  
  rownames(daterange2) <- daterange2$Day
  daterange2$Day <- NULL
  
  par(mar=c(6, 5, 2, 5) + 0.1)
  # barp <- barplot(t(daterange2[,1:ncol(daterange2)]), plot = FALSE, beside = T)
  # barplot(t(daterange2[,1:ncol(daterange2)]), beside = T, col=brewer.pal(n = rel_num, name = "Dark2"), 
  #         xlab = "", ylab = "Number of fish arrivals per day", 
  #         ylim = c(0,max(daterange2[,1:ncol(daterange2)], na.rm = T)*1.2), 
  #         las = 2, xlim=c(0,max(barp)+1), cex.lab = 1.5, yaxt = "n", xaxt = "n", border = NA)#, 
  #         #legend.text = colnames(daterange2[,1:ncol(daterange2)-1]),
  #         #args.legend = list(x ='topright', bty='n', inset=c(-0.2,0)), title = "Release Group")
  # legend(x ='topleft', legend = colnames(daterange2)[1:ncol(daterange2)], fill= brewer.pal(n = rel_num, name = "Set1"), horiz = T, title = "Release")
  # ybreaks <- if(max(daterange2[,1:ncol(daterange2)], na.rm = T) < 4) {max(daterange2[,1:ncol(daterange2)], na.rm = T)} else {5}
  # xbreaks <- if(ncol(barp) > 10) {seq(1, ncol(barp), 2)} else {1:ncol(barp)}
  # barpmeans <- colMeans(barp)
  # axis(1, at = barpmeans[xbreaks], labels = rownames(daterange2)[xbreaks], las = 2)
  # axis(2, at = pretty(0:max(daterange2[,1:ncol(daterange2)], na.rm = T), ybreaks))
  # box()
  daterange2$Date <- as.Date(row.names(daterange2))
  daterange3 <- melt(daterange2, id.vars = "Date", variable.name = ".", )
  
  # p <- ggplot(data = daterange3, aes(x = Date, y = value, color = ., fill = .)) +
  #   geom_bar(stat='identity') +
  #   ylab("Number of fish arrivals per day") +
  #   #xlim(range(daterange$Day)) +
  #   #geom_line(data= daterange2_flow, aes(x = Date, y = parameter_value/500), color = alpha("#947FFF", alpha = 0.5))+
  #   #scale_x_date(date_breaks = "5 days") +
  #   #scale_y_continuous(name = "Number of fish arrivals per day",
  #     # Add a second axis and specify its features
  #   #  sec.axis = sec_axis(~.*500, name="Second Axis")) +
  #   theme_bw() +
  #   theme(panel.border = element_rect(colour = "black", fill=NA))

  
  daterange3$. <- factor(daterange3$., levels = sort(unique(daterange3$.), decreasing = T))
  plot_ly(daterange3, width = 900, height = 600, dynamicTicks = TRUE) %>%
    add_bars(x = ~Date, y = ~value, color = ~.) %>%
    add_annotations( text="Release (click on legend items to isolate)", xref="paper", yref="paper",
                     x=0.01, xanchor="left",
                     y=1.056, yanchor="top",    # Same y as legend below
                     legendtitle=TRUE, showarrow=FALSE ) %>%
    #add_lines(x=~daterange2_flow$Date, y=~daterange2_flow$parameter_value, line = list(color = alpha("#947FFF", alpha = 0.5)), yaxis="y2", showlegend=FALSE, inherit=FALSE) %>%
    layout(showlegend = T, 
           barmode = "stack",
           xaxis = list(title = "Date", mirror=T,ticks='outside',showline=T), yaxis = list(title = "Number of fish arrivals per day", mirror=T,ticks='outside',showline=T),
          legend = list(orientation = "h",x = 0.34, y = 1.066),
          margin=list(l = 50,r = 100,b = 50,t = 50)
           )



}else{
  plot(1:2, type = "n", xlab = "",xaxt = "n", yaxt = "n", ylab = "Number of fish arrivals per day")
  text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
}

2.3 Detections at Benicia Bridge for duration of tag life

---

3. Survival and Routing Probability

---

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

detects_tower <- detects_study[detects_study$general_location == "TowerBridge",]

if (nrow(detects_tower) == 0){
  WR.surv <- data.frame("Release"=NA, "Survival (%)"="NO DETECTIONS YET", "SE"=NA, "95% lower C.I."=NA, "95% upper C.I."=NA, "Detection efficiency (%)"=NA)
  colnames(WR.surv) <- c("Release", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")
  
  print(kable(WR.surv, row.names = F, "html", caption = "3.1 Minimum survival to Tower Bridge (using CJS survival model). If Yolo Bypass Weirs are overtopping during migration, fish may have taken that route, and therefore this is a minimum estimate of survival") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
} else {
  
  study_count <- nrow(study_tagcodes)
  ## Only do survival to Sac for now
  test <- detects_study[which(detects_study$river_km > 168 & detects_study$river_km < 175),]
  
  ## Create inp for survival estimation
  
  inp <- as.data.frame(reshape2::dcast(test, TagCode ~ river_km, fun.aggregate = length))
  
  ## Sort columns by river km in descending order
  # Count number of genlocs
  gen_loc_sites <- ncol(inp)-1
  
  if(gen_loc_sites <2){
    WR.surv <- data.frame("Release"=NA, "Survival (%)"="NOT ENOUGH DETECTIONS", "SE"=NA, "95% lower C.I."=NA, "95% upper C.I."=NA, "Detection efficiency (%)"=NA)
    colnames(WR.surv) <- c("Release", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")
    
    print(kable(WR.surv, row.names = F, "html", caption = "3.1 Minimum survival to Tower Bridge (using CJS survival model). If Yolo Bypass Weirs are overtopping during migration, fish may have taken that route, and therefore this is a minimum estimate of survival") %>%
            kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
  }else{
    
    inp <- inp[,c(1,order(names(inp[,2:(gen_loc_sites+1)]), decreasing = T)+1)]
    
    inp <- merge(study_tagcodes, inp, by = "TagCode", all.x = T)
    
    inp2 <- inp[,(ncol(inp)-gen_loc_sites+1):ncol(inp)]
    inp2[is.na(inp2)] <- 0
    inp2[inp2 > 0] <- 1
    
    inp <- cbind(inp, inp2)
    groups <- as.character(sort(unique(inp$Release)))
    test$Release <- factor(test$Release, levels = groups)
    
    inp[,groups] <- 0
    for (i in groups) {
      inp[as.character(inp$Release) == i, i] <- 1
    }
    
    inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""),sep="")
    
    
    if(length(groups) > 1){
      ## make sure factor levels have a release that has detections first. if first release in factor order has zero detectins, model goes haywire
      inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, rel = factor(inp$Release, levels = names(sort(table(test$Release),decreasing = T))), stringsAsFactors = F)
      
      WR.process <- process.data(inp.df, model="CJS", begin.time=1, groups = "rel")     
      
      WR.ddl <- make.design.data(WR.process)
      
      WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
      
      WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time*rel),p=list(formula=~time)), silent = T, output = F)
      
      WR.surv <- round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1)
      WR.surv <- rbind(WR.surv, round(WR.mark.rel$results$real[seq(from=1,to=length(groups)*2,by = 2),c("estimate", "se", "lcl", "ucl")] * 100,1))
      WR.surv$Detection_efficiency <- NA
      WR.surv[1,"Detection_efficiency"] <-   round(WR.mark.all$results$real[gen_loc_sites+1,"estimate"] * 100,1)
      
      WR.surv <- cbind(c("ALL", names(sort(table(test$Release),decreasing = T))), WR.surv)
    }
    if(length(unique(inp[,groups])) < 2){
      inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""), " ", 1,sep = "")
      write.table(inp$inp_final,"WRinp.inp",row.names = F, col.names = F, quote = F)
      WRinp <- convert.inp("WRinp.inp")
      WR.process <- process.data(WRinp, model="CJS", begin.time=1) 
      
      WR.ddl <- make.design.data(WR.process)
      
      WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
      
      WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
      
      WR.surv <- round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1)
      WR.surv <- rbind(WR.surv, round(WR.mark.rel$results$real[seq(from=1,to=length(groups)*2,by = 2),c("estimate", "se", "lcl", "ucl")] * 100,1))
      WR.surv$Detection_efficiency <- NA
      WR.surv[1,"Detection_efficiency"] <- round(WR.mark.all$results$real[gen_loc_sites+1,"estimate"] * 100,1)
      
      WR.surv <- cbind(c("ALL", groups), WR.surv)
    }
    
    
    colnames(WR.surv) <- c("Release", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")
    
    print(kable(WR.surv, row.names = F, "html", caption = "3.1 Minimum survival to Tower Bridge (using CJS survival model). If Yolo Bypass Weirs are overtopping during migration, fish may have taken that route, and therefore this is a minimum estimate of survival") %>%
            kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
  }
}

## Once Georgiana Sl receivers are back online, remove "eval = F" from header

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

route_results_possible <- F
if (nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
  results_short <- data.frame("Measure"=NA, "Estimate"="NO DETECTIONS YET", "SE"=NA, "95% lower C.I."=NA, "95% upper C.I."=NA)
  colnames(results_short) <- c("Measure", "Estimate", "SE", "95% lower C.I.", "95% upper C.I.")
  print(kable(results_short, row.names = F, "html", caption = "3.2 Reach-specific survival and probability of entering Georgiana Slough") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
} else {
  
  ## Only do survival to Georg split for now
  test2 <- detects_study[detects_study$general_location %in% c("ButteBrRT","TowerBridge", "I80-50_Br", "Sac_BlwGeorgiana", "Sac_BlwGeorgiana2", "Georgiana_Slough1", "Georgiana_Slough2"),]
  
  ## We can only do multistate model if there is at least one detection in each route
  
  if(nrow(test2[test2$general_location %in% c("Sac_BlwGeorgiana", "Sac_BlwGeorgiana2"),]) == 0 |
     nrow(test2[test2$general_location %in% c("Georgiana_Slough1", "Georgiana_Slough2"),]) == 0){
    results_short <- data.frame("Measure"=NA, "Estimate"="NOT ENOUGH DETECTIONS", "SE"=NA, "95% lower C.I."=NA, "95% upper C.I."=NA)
    colnames(results_short) <- c("Measure", "Estimate", "SE", "95% lower C.I.", "95% upper C.I.")
    print(kable(results_short, row.names = F, "html", caption = "3.2 Reach-specific survival and probability of entering Georgiana Slough") %>%
            kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
  }else{
    
    ## Make tagcode character
    study_tagcodes$TagCode <- as.character(study_tagcodes$TagCode)
    ## Make a crosstab query with frequencies for all tag/location combination
    test2$general_location <- factor(test2$general_location, levels = c("ButteBrRT","TowerBridge", "I80-50_Br", "Sac_BlwGeorgiana", "Sac_BlwGeorgiana2", "Georgiana_Slough1", "Georgiana_Slough2"))
    test2$TagCode <- factor(test2$TagCode, levels = study_tagcodes$TagCode)
    mytable <- table(test2$TagCode, test2$general_location) # A will be rows, B will be columns
    
    ## Change all frequencies bigger than 1 to 1. Here you could change your minimum cutoff to 2 detections, and then make another command that changes all detections=1 to 0
    mytable[mytable>0] <- "A"
    
    ## Order in order of rkm
    mytable2 <- mytable[, c("ButteBrRT","TowerBridge", "I80-50_Br", "Sac_BlwGeorgiana", "Sac_BlwGeorgiana2", "Georgiana_Slough1", "Georgiana_Slough2")]
    
    ## Now sort the crosstab rows alphabetically
    mytable2 <- mytable2[order(row.names(mytable2)),]
    
    mytable2[which(mytable2[, "Sac_BlwGeorgiana"]=="A"), "Sac_BlwGeorgiana"] <- "A"
    mytable2[which(mytable2[, "Sac_BlwGeorgiana2"]=="A"), "Sac_BlwGeorgiana2"] <- "A"
    mytable2[which(mytable2[, "Georgiana_Slough1"]=="A"), "Georgiana_Slough1"] <- "B"
    mytable2[which(mytable2[, "Georgiana_Slough2"]=="A"), "Georgiana_Slough2"] <- "B"
    
    ## Make a crosstab query with frequencies for all weekly Release groups
    #test2$Release <- factor(test2$Release)
    #mytable3 <- table(test2$TagCode, test2$Release) # A will be rows, B will be columns
    
    ## Change all frequencies bigger than 1 to 1. Here you could change your minimum cutoff to 2 detections, and then make another command that changes all detections=1 to 0
    #mytable3[mytable3>0] <- 1
    
    ## Order in order of rkm
    #mytable4 <- mytable3[, order(colnames(mytable3))]
    
    ## Now sort the crosstab rows alphabetically
    #mytable4 <- mytable4[order(row.names(mytable4)),]
    
    ## Now order the study_tagcodes table the same way
    study_tagcodes <- study_tagcodes[order(study_tagcodes$TagCode),]
    
    ## Paste together (concatenate) the data from each column of the crosstab into one string per row, add to tagging_meta.
    ## For this step, make sure both are sorted by FishID
    study_tagcodes$inp_part1 <- apply(mytable2[,1:3],1,paste,collapse="")
    study_tagcodes$inp_partA <- apply(mytable2[,4:5],1,paste,collapse="")
    study_tagcodes$inp_partB <- apply(mytable2[,6:7],1,paste,collapse="")
    #study_tagcodes$inp_group <- apply(mytable4,1,paste,collapse=" ")
    
    ## We need to have a way of picking which route to assign to a fish if it was detected by both georg and blw-georg recvs
    ## We will say that the last detection at that junction is what determines the route it took
    
    ## find last detection at each genloc
    departure <- aggregate(list(depart = test2$DateTime_PST), by = list(TagCode = test2$TagCode, last_location = test2$general_location), FUN = max)
    ## subset for just juncture locations
    departure <- departure[departure$last_location %in% c("Sac_BlwGeorgiana", "Sac_BlwGeorgiana2", "Georgiana_Slough1", "Georgiana_Slough2"),]
    ## Find genloc of last known detection per tag
    last_depart <- aggregate(list(depart = departure$depart), by = list(TagCode = departure$TagCode), FUN = max)
    
    last_depart1 <- merge(last_depart, departure)
    study_tagcodes <- merge(study_tagcodes, last_depart1[,c("TagCode", "last_location")], by = "TagCode", all.x = T)
    
    ## Assume that the Sac is default pathway, and for fish that were detected in neither route, it would get a "00" in inp so doesn't matter anyway
    study_tagcodes$inp_final <- paste("A",study_tagcodes$inp_part1, study_tagcodes$inp_partA," 1 ;", sep = "")
    
    ## now put in exceptions...fish that were seen in georgiana last
    study_tagcodes[study_tagcodes$last_location %in% c("Georgiana_Slough1", "Georgiana_Slough2"), "inp_final"] <- paste("A",study_tagcodes[study_tagcodes$last_location %in% c("Georgiana_Slough1", "Georgiana_Slough2"), "inp_part1"], study_tagcodes[study_tagcodes$last_location %in% c("Georgiana_Slough1", "Georgiana_Slough2"), "inp_partB"]," 1 ;", sep = "")
    
    ## At this point, some fish might not have been deemed to ever take a route based on last visit analysis. If so, model can't be run
    
    if(any(grepl(pattern = "A", study_tagcodes$inp_final)==T) & any(grepl(pattern = "B", study_tagcodes$inp_final)==T)){
      
      write.table(study_tagcodes$inp_final,"WRinp_multistate.inp",row.names = F, col.names = F, quote = F)
      
      WRinp <- convert.inp("WRinp_multistate.inp")
      
      dp <- process.data(WRinp, model="Multistrata") 
      
      ddl <- make.design.data(dp)
      
      #### p ####
      # Can't be seen at 2B or 3B or 4B (butte, tower or I80)
      ddl$p$fix=NA
      ddl$p$fix[ddl$p$stratum == "B" & ddl$p$time %in% c(2,3,4)]=0
      
      #### Psi ####
      # Only 1 transition allowed:
      # from A to B at time interval 4 to 5
      
      ddl$Psi$fix=0
      # A to B can only happen for interval 3-4
      ddl$Psi$fix[ddl$Psi$stratum=="A"&
                    ddl$Psi$tostratum=="B" & ddl$Psi$time==4]=NA
      
      #### Phi a.k.a. S ####
      ddl$S$fix=NA
      # None in B for reaches 1,2,3,4 and fixing it to 1 for 5 (between two georg lines). All getting fixed to 1
      ddl$S$fix[ddl$S$stratum=="B" & ddl$S$time %in% c(1,2,3,4,5)]=1
      
      # For route A, fixing it to 1 for 5 (between two blw_georg lines)
      ddl$S$fix[ddl$S$stratum=="A" & ddl$S$time==5]=1
      ## We use -1 at beginning of formula to remove intercept. This is because different routes probably shouldn't share the same intercept
      
      p.timexstratum=list(formula=~-1+stratum:time)
      Psi.stratumxtime=list(formula=~-1+stratum:time)
      S.stratumxtime=list(formula=~-1+stratum:time)
      
      ## Run model a first time
      S.timexstratum.p.timexstratum.Psi.timexstratum=mark(dp,ddl, model.parameters=list(S=S.stratumxtime,p= p.timexstratum,Psi=Psi.stratumxtime), realvcv = T, silent = T, output = F)
      
      ## Identify any parameter estimates at 1, which would likely have bad SE estimates.
      profile.intervals <- which(S.timexstratum.p.timexstratum.Psi.timexstratum$results$real$estimate %in% c(0,1) & !S.timexstratum.p.timexstratum.Psi.timexstratum$results$real$fixed == "Fixed")
      
      ## Rerun model using profile interval estimation for the tricky parameters
      S.timexstratum.p.timexstratum.Psi.timexstratum=mark(dp,ddl, model.parameters=list(S=S.stratumxtime,p= p.timexstratum,Psi=Psi.stratumxtime), realvcv = T, profile.int = profile.intervals, silent = T, output = F)
      
      results <- S.timexstratum.p.timexstratum.Psi.timexstratum$results$real
      
      results_short <- results[rownames(results) %in% c("S sA g1 c1 a0 o1 t1",
                                                        "S sA g1 c1 a1 o2 t2",
                                                        "S sA g1 c1 a2 o3 t3",
                                                        "S sA g1 c1 a3 o4 t4",
                                                        "p sA g1 c1 a1 o1 t2",
                                                        "p sA g1 c1 a2 o2 t3",
                                                        "p sA g1 c1 a3 o3 t4",
                                                        "p sA g1 c1 a4 o4 t5",
                                                        "p sB g1 c1 a4 o4 t5",
                                                        "Psi sA toB g1 c1 a3 o4 t4"
      ),]
      
      
      results_short <- round(results_short[,c("estimate", "se", "lcl", "ucl")] * 100,1)
      
      ## Now find estimate and CIs for AtoA route at junction
      Psilist=get.real(S.timexstratum.p.timexstratum.Psi.timexstratum,"Psi",vcv=TRUE)  
      Psivalues=Psilist$estimates
      
      routes <- TransitionMatrix(Psivalues[Psivalues$time==4 & Psivalues$cohort==1,],vcv.real=Psilist$vcv.real)
      
      
      
      
      
      results_short$Measure <- c("Survival from release to Butte City","Survival from Butte City to TowerBridge (minimum estimate since fish may have taken Yolo Bypass)", "Survival from TowerBridge to I80-50_Br", "% arrived from I80-50_Br to Georgiana Slough confluence (not survival because fish may have taken Sutter/Steam)","Detection probability at Butte City",
                                 "Detection probability at TowerBridge", "Detection probability at I80-50_Br", "Detection probability at Blw_Georgiana", "Detection probability at Georgiana Slough",
                                 "Routing probability into Georgiana Slough (Conditional on fish arriving to junction)")
      
      results_short <- results_short[,c("Measure", "estimate", "se", "lcl", "ucl")]
      colnames(results_short) <- c("Measure", "Estimate", "SE", "95% lower C.I.", "95% upper C.I.")
      
      print(kable(results_short, row.names = F, "html", caption = "3.2 Reach-specific survival and probability of entering Georgiana Slough") %>%
              kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
      
      route_results_possible <- T
      
    } else {
      results_short <- data.frame("Measure"=NA, "Estimate"="NOT ENOUGH DETECTIONS YET", "SE"=NA, "95% lower C.I."=NA, "95% upper C.I."=NA)
      colnames(results_short) <- c("Measure", "Estimate", "SE", "95% lower C.I.", "95% upper C.I.")
      print(kable(results_short, row.names = F, "html", caption = "3.2 Reach-specific survival and probability of entering Georgiana Slough") %>%
              kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
    }
  }
}

## Once Georgiana Sl receivers are back online, remove "eval = F" from header
##____________________________________________________________________________
## If you don't have access to local files, uncomment and run next lines of code

#download.file("https://raw.githubusercontent.com/CalFishTrack/real-time/master/data/georg.png",destfile = "georg.png", quiet = T, mode = "wb")

##________________________________________________________________________________


georg <- readPNG("georg.png")
par(mar=c(2,0,0,0))
#Set up the plot area
plot(1:2, type='n', xlab="", ylab="", xaxt = "n", yaxt = "n")

#Get the plot information so the image will fill the plot box, and draw it
lim <- par()
rasterImage(georg, lim$usr[1], lim$usr[3], lim$usr[2], lim$usr[4])
if (nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
    legend(x = 1.55,y = 1.6,legend =  "No detections yet",col = "white", box.col = "light gray", bg = "light gray") 
    legend(x = 1.55,y = 1.45,legend =  "No detections yet",col = "white", box.col = "light gray", bg = "light gray")
}else if (route_results_possible == F){
    legend(x = 1.55,y = 1.6,legend =  "Too few detections",col = "white", box.col = "light gray", bg = "light gray") 
    legend(x = 1.55,y = 1.45,legend =  "Too few detections",col = "white", box.col = "light gray", bg = "light gray") 
}else{  
  legend(x = 1.55,y = 1.6,legend =  paste(round(routes$TransitionMat["A","A"],3)*100,"% (",round(routes$lcl.TransitionMat["A","A"],3)*100,"-",round(routes$ucl.TransitionMat["A","A"],3)*100,")", sep =""),col = "white", box.col = "light gray", bg = "light gray")
  legend(1.55,1.45, legend =  paste(round(routes$TransitionMat["A","B"],3)*100,"% (",round(routes$lcl.TransitionMat["A","B"],3)*100,"-",round(routes$ucl.TransitionMat["A","B"],3)*100,")", sep =""), box.col = "light gray", bg = "light gray")
}  
  mtext(text = "3.3 Routing Probabilities at Georgiana Slough Junction (with 95% C.I.s)", cex = 1.3, side = 1, line = 0.2, adj = 0)

## Once Georgiana Sl receivers are back online, remove "eval = F" from header

if (nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
  plot(1:2, type = "n",xaxt = "n", yaxt = "n", xlab = "Range of days study fish were present at Georgiana Sl Junction", ylab = "Routing probability into Georgiana Slough at the junction")
  text(1.5,1.5, labels = "NO DETECTIONS YET", cex = 2)
}else if (route_results_possible == F){
  plot(1:2, type = "n",xaxt = "n", yaxt = "n", xlab = "Range of days study fish were present at Georgiana Sl Junction", ylab = "Routing probability into Georgiana Slough at the junction")
  text(1.5,1.5, labels = "TOO FEW DETECTIONS", cex = 2)
}else{  
  
  library(repmis)
  
  trytest <- try(source_data("https://code.usgs.gov/crrl_qfes/Enhanced_Acoustic_Telemetry_Project/raw/master/EAT_data_2021.Rdata?raw=True"))
  
  if (inherits(trytest, "try-error")){
    plot(1:2, type = "n",xaxt = "n", yaxt = "n", xlab = "Range of days study fish were present at Georgiana Sl Junction", ylab = "Routing probability into Georgiana Slough at the junction")
    text(1.5,1.5, labels = "ERROR DOWNLOADING STARS", cex = 2)
  }else{
  
    ## first, find min and max arrivals at georg for a study
    min_georg <- as.Date(format(min(test2[test2$general_location %in% c("Sac_BlwGeorgiana", "Sac_BlwGeorgiana2","Georgiana_Slough1", "Georgiana_Slough2"),"DateTime_PST"]), "%Y-%m-%d"))
    max_georg <- as.Date(format(max(test2[test2$general_location %in% c("Sac_BlwGeorgiana", "Sac_BlwGeorgiana2","Georgiana_Slough1", "Georgiana_Slough2"),"DateTime_PST"]), "%Y-%m-%d"))
    
    psi_study <- psi_GeoCond[psi_GeoCond$Date <= max_georg & psi_GeoCond$Date >=min_georg-1,]
    
    plot(psi_study$Date, psi_study$psi_geo.50, ylim = c(0,1), xlim = c(min_georg, max_georg), type = "n", xaxt = "n", xlab = "Range of days study fish were present at Georgiana Sl Junction", ylab = "Routing probability into Georgiana Slough at the junction")
    polygon(c(psi_study$Date, rev(psi_study$Date)), 
            c(psi_study$psi_geo.10,rev(psi_study$psi_geo.90)), density = 200, col ='grey90')
    lines(psi_study$Date, psi_study$psi_geo.50, lty = 3)
    points(mean(psi_study$Date), tail(results_short$Estimate,1)/100, pch = 16, cex = 1.3)
    arrows(mean(psi_study$Date), tail(results_short$`95% lower C.I.`,1)/100, mean(psi_study$Date), tail(results_short$`95% upper C.I.`,1)/100, length=0.05, angle=90, code=3)
    axis(side=1, at=psi_study$Date, labels=format(psi_study$Date, '%b-%d'))
    legend("topright", legend = c('STARS daily predictions during study (w/ 90% CI)', 'Empirical estimate over study period (w/ 95% CI)'), 
           bty = "n",
           col = c("black","black"),
           lty = c(3,1),
           fill = c("grey90", NA),
           border = c(NA,NA),
           pch = c(NA,16),
           seg.len =0.8,
           cex= 1.2
    )
  }
}  

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))
  
try(benicia <- read.csv("benicia_surv.csv", stringsAsFactors = F))

detects_benicia <- detects_study[detects_study$general_location %in% c("Benicia_west", "Benicia_east"),]
endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")), max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life)*1.5)))

if (nrow(detects_benicia) == 0){
  if(as.numeric(difftime(Sys.time(), min(detects_study$RelDT), units = "days"))>30){
    WR.surv <- data.frame("Release"="ALL", "estimate"=0, "se"=NA, "lcl"=NA, "ucl"=NA, "Detection_efficiency"=NA)
  }else{
    WR.surv <- data.frame("Release"=NA, "estimate"="NO DETECTIONS YET", "se"=NA, "lcl"=NA, "ucl"=NA, "Detection_efficiency"=NA)
  }
  WR.surv1 <- WR.surv
  colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")
  print(kable(WR.surv1, row.names = F, "html", caption = "3.5 Minimum survival to Benicia Bridge East Span (using CJS survival model)") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))  
}else if (length(table(detects_benicia$general_location)) == 1){
  if(as.numeric(difftime(Sys.time(), min(detects_study$RelDT), units = "days"))>30){
    WR.surv <- data.frame("Release"="ALL", "estimate"=round(length(unique(detects_benicia$TagCode))/length(unique(detects_study$TagCode))*100,1), "se"=NA, "lcl"=NA, "ucl"=NA, "Detection_efficiency"=NA)
  }else{
    WR.surv <- data.frame("Release"=NA, "estimate"="NOT ENOUGH DETECTIONS", "se"=NA, "lcl"=NA, "ucl"=NA, "Detection_efficiency"=NA)
  }
    WR.surv1 <- WR.surv
    colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")
    print(kable(WR.surv1, row.names = F, "html", caption = "3.5 Minimum survival to Benicia Bridge East Span (using CJS survival model)") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
} else {  
  ## Only do survival to Benicia here
  test3 <- detects_study[which(detects_study$river_km < 53),]
  
  ## Create inp for survival estimation
  
  inp <- as.data.frame(reshape2::dcast(test3, TagCode ~ river_km, fun.aggregate = length))
  
  ## Sort columns by river km in descending order
  # Count number of genlocs
  gen_loc_sites <- ncol(inp)-1
  
  inp <- inp[,c(1,order(names(inp[,2:(gen_loc_sites+1)]), decreasing = T)+1)]

  inp <- merge(study_tagcodes, inp, by = "TagCode", all.x = T)
  
  inp2 <- inp[,(ncol(inp)-gen_loc_sites+1):ncol(inp)]
  inp2[is.na(inp2)] <- 0
  inp2[inp2 > 0] <- 1
  
  inp <- cbind(inp, inp2)
  groups <- as.character(sort(unique(inp$Release)))
  groups_w_detects <- names(table(test3$Release))

  inp[,groups] <- 0
  for (i in groups) {
    inp[as.character(inp$Release) == i, i] <- 1
  }

  inp$inp_final <- paste("1",apply(inp2, 1, paste, collapse=""),sep="")
  
  
  if(length(groups) > 1){
  ## make sure factor levels have a release that has detections first. if first release in factor order has zero #detectins, model goes haywire
    inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, rel = inp$Release, stringsAsFactors = F)

    WR.process <- process.data(inp.df, model="CJS", begin.time=1) 
    
    WR.ddl <- make.design.data(WR.process)
    
    WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
    
    inp.df <- inp.df[inp.df$rel %in% groups_w_detects,]
    inp.df$rel <- factor(inp.df$rel, levels = groups_w_detects)
    if(length(groups_w_detects) > 1){
      WR.process <- process.data(inp.df, model="CJS", begin.time=1, groups = "rel") 
    
      WR.ddl <- make.design.data(WR.process)
      WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time*rel),p=list(formula=~time)), silent = T, output = F)
    }else{  
      WR.process <- process.data(inp.df, model="CJS", begin.time=1) 
    
      WR.ddl <- make.design.data(WR.process)
      WR.mark.rel <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)
    }
    WR.surv <- cbind(Release = "ALL",round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1))
    WR.surv.rel <- cbind(Release = groups_w_detects, round(WR.mark.rel$results$real[seq(from=1,to=length(groups_w_detects)*2,by = 2),c("estimate", "se", "lcl", "ucl")] * 100,1))
    WR.surv.rel <- merge(WR.surv.rel, data.frame(Release = groups), all.y = T)
    WR.surv.rel[is.na(WR.surv.rel$estimate),"estimate"] <- 0
    WR.surv <- rbind(WR.surv, WR.surv.rel)
    
  }else{
    inp.df <- data.frame(ch = as.character(inp$inp_final), freq = 1, stringsAsFactors = F)

    WR.process <- process.data(inp.df, model="CJS", begin.time=1) 
    
      
    WR.ddl <- make.design.data(WR.process)
    
    WR.mark.all <- mark(WR.process, WR.ddl, model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)), silent = T, output = F)

    WR.surv <- cbind(Release = c("ALL", groups),round(WR.mark.all$results$real[1,c("estimate", "se", "lcl", "ucl")] * 100,1))
    
  }
  WR.surv$Detection_efficiency <- NA
  WR.surv[1,"Detection_efficiency"] <- round(WR.mark.all$results$real[gen_loc_sites+1,"estimate"] * 100,1)

  WR.surv1 <- WR.surv
  colnames(WR.surv1) <- c("Release Group", "Survival (%)", "SE", "95% lower C.I.", "95% upper C.I.", "Detection efficiency (%)")

  print(kable(WR.surv1, row.names = F, "html", caption = "3.5 Minimum survival to Benicia Bridge East Span (using CJS survival model)") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))    
  
}

3.5 Minimum survival to Benicia Bridge East Span (using CJS survival model)

Release Group	Survival (%)	SE	95% lower C.I.	95% upper C.I.	Detection efficiency (%)
ALL	7.7	1.2	5.7	10.5	94.6
Durham_Ferry	5.2	1.4	3.1	8.8	NA
Head_of_Old_River	9.0	2.4	5.3	14.9	NA
Stockton	12.1	3.3	7.0	20.0	NA

if(exists("benicia")==T & is.numeric(WR.surv1[1,2])){
  ## Find mean release time per release group, and ALL
  reltimes <- aggregate(list(RelDT = study_tagcodes$release_time), by = list(Release = study_tagcodes$Release), FUN = mean)
  reltimes <- rbind(reltimes, data.frame(Release = "ALL", RelDT = mean(study_tagcodes$release_time)))

  ## Assign whether the results are tentative or final
  quality <- "tentative"
  if(endtime < as.Date(format(Sys.time(), "%Y-%m-%d"))) { quality <- "final"}

  WR.surv <- merge(WR.surv, reltimes, by = "Release", all.x = T)
  
  WR.surv$RelDT <- as.POSIXct(WR.surv$RelDT, origin = '1970-01-01')
  benicia$RelDT <- as.POSIXct(benicia$RelDT)
  ## remove old benicia record for this studyID
  benicia <- benicia[!benicia$StudyID == unique(detects_study$Study_ID),]
  
  benicia <- rbind(benicia, data.frame(WR.surv, StudyID = unique(detects_study$Study_ID), data_quality = quality))
  
  write.csv(benicia, "benicia_surv.csv", row.names = F, quote = F) 
}

---

4. Detections statistics at all realtime receivers

---

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

if (nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
  "No detections yet"
} else {

  arrivals <- aggregate(list(DateTime_PST = detects_study$DateTime_PST), by = list(general_location = detects_study$general_location, TagCode = detects_study$TagCode), FUN = min)
  
  tag_stats <- aggregate(list(First_arrival = arrivals$DateTime_PST), 
                         by= list(general_location = arrivals$general_location),
                         FUN = min)
  tag_stats <- merge(tag_stats, 
                     aggregate(list(Mean_arrival = arrivals$DateTime_PST), 
                         by= list(general_location = arrivals$general_location),
                         FUN = mean), 
                     by = c("general_location"))
  tag_stats <- merge(tag_stats, 
                     aggregate(list(Last_arrival = arrivals$DateTime_PST), 
                         by= list(general_location = arrivals$general_location),
                         FUN = max), 
                     by = c("general_location"))
  tag_stats <- merge(tag_stats, 
                     aggregate(list(Fish_count = arrivals$TagCode), 
                         by= list(general_location = arrivals$general_location), 
                         FUN = function(x) {length(unique(x))}), 
                     by = c("general_location"))
  tag_stats$Percent_arrived <- round(tag_stats$Fish_count/nrow(study_tagcodes) * 100,2)
      
  tag_stats <- merge(tag_stats, unique(detects_study[,c("general_location", "river_km")]))
  
  tag_stats <- tag_stats[order(tag_stats$river_km, decreasing = T),]
  
  tag_stats[,c("First_arrival", "Mean_arrival", "Last_arrival")] <- format(tag_stats[,c("First_arrival", "Mean_arrival", "Last_arrival")], tz = "Etc/GMT+8")
  
  tag_stats <- tag_stats[is.na(tag_stats$First_arrival)==F,]

  print(kable(tag_stats, row.names = F, 
              caption = "4.1 Detections for all releases combined",
              "html") %>%
          kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
  
  for (j in sort(unique(study_tagcodes$Release))) {
    
    if(nrow(detects_study[detects_study$Release == j,]) > 0 ) {
    
      temp <- detects_study[detects_study$Release == j,]
      
        arrivals1 <- aggregate(list(DateTime_PST = temp$DateTime_PST), by = list(general_location = temp$general_location, TagCode = temp$TagCode), FUN = min)
  
      rel_count <- nrow(study_tagcodes[study_tagcodes$Release == j,])
  
      tag_stats1 <- aggregate(list(First_arrival = arrivals1$DateTime_PST), 
                             by= list(general_location = arrivals1$general_location), 
                             FUN = min)
      tag_stats1 <- merge(tag_stats1, 
                         aggregate(list(Mean_arrival = arrivals1$DateTime_PST), 
                             by= list(general_location = arrivals1$general_location), 
                             FUN = mean), 
                         by = c("general_location"))
      tag_stats1 <- merge(tag_stats1, 
                   aggregate(list(Last_arrival = arrivals1$DateTime_PST), 
                       by= list(general_location = arrivals1$general_location), 
                       FUN = max), 
                   by = c("general_location"))
      tag_stats1 <- merge(tag_stats1, 
                         aggregate(list(Fish_count = arrivals1$TagCode), 
                                   by= list(general_location = arrivals1$general_location), 
                                   FUN = function(x) {length(unique(x))}), 
                         by = c("general_location"))
      
      tag_stats1$Percent_arrived <- round(tag_stats1$Fish_count/rel_count * 100,2)
    
      tag_stats1 <- merge(tag_stats1, unique(detects_study[,c("general_location", "river_km")]))
    
      tag_stats1 <- tag_stats1[order(tag_stats1$river_km, decreasing = T),]
      
      tag_stats1[,c("First_arrival", "Mean_arrival", "Last_arrival")] <- format(tag_stats1[,c("First_arrival", "Mean_arrival", "Last_arrival")], tz = "Etc/GMT+8")
      
      tag_stats1 <- tag_stats1[is.na(tag_stats1$First_arrival)==F,]
      
      final_stats <- kable(tag_stats1, row.names = F, 
            caption = paste("4.2 Detections for",j,"release groups", sep = " "),
            "html")
      
      print(kable_styling(final_stats, bootstrap_options = c("striped", "hover", "condensed", "responsive", "bordered"), full_width = F, position = "left"))
      
    } else {
      cat("\n\n\\pagebreak\n")
      print(paste("No detections for",j,"release group yet", sep=" "), quote = F)
      cat("\n\n\\pagebreak\n")
    }
  }
}

4.1 Detections for all releases combined

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
Old River	2022-03-26 12:58:55	2022-04-01 14:09:07	2022-04-06 19:24:20	13	2.64	153.001
MiddleRiver	2022-03-25 11:17:13	2022-04-11 15:05:13	2022-05-29 02:38:53	15	3.05	150.000
Clifton_Court_US_Radial_Gates	2022-03-20 15:26:55	2022-04-03 20:32:01	2022-06-07 09:29:44	68	13.82	146.000
Holland_Cut_Quimby	2022-03-27 15:13:48	2022-04-12 16:27:29	2022-05-24 16:03:28	14	2.85	145.000
CVP_Tank	2022-03-28 23:39:05	2022-04-03 21:31:09	2022-04-22 13:52:32	13	2.64	144.531
CVP_Trash_Rack_1	2022-03-21 03:31:53	2022-04-02 23:36:56	2022-05-19 06:56:12	70	14.23	144.500
Clifton_Court_Intake_Canal	2022-03-26 09:31:50	2022-04-04 12:24:50	2022-05-12 03:28:04	23	4.67	142.721
Old_River_Quimby	2022-03-23 01:52:29	2022-03-29 09:52:34	2022-04-03 21:39:12	6	1.22	141.000
Benicia_east	2022-03-24 12:53:31	2022-04-10 21:12:30	2022-05-25 16:41:41	36	7.32	52.240
Benicia_west	2022-03-23 22:09:11	2022-04-10 07:58:57	2022-05-25 16:45:27	37	7.52	52.040

4.2 Detections for Durham_Ferry release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
Old River	2022-03-31 17:24:16	2022-04-03 07:34:29	2022-04-06 11:16:18	5	2.02	153.001
MiddleRiver	2022-04-08 11:01:38	2022-05-03 22:11:03	2022-05-17 14:07:55	3	1.21	150.000
Clifton_Court_US_Radial_Gates	2022-03-25 22:13:53	2022-04-05 18:59:47	2022-05-22 19:28:19	36	14.52	146.000
Holland_Cut_Quimby	2022-04-06 15:19:13	2022-04-21 10:18:31	2022-05-19 05:07:54	3	1.21	145.000
CVP_Tank	2022-04-02 19:22:01	2022-04-05 16:56:10	2022-04-07 07:36:57	4	1.61	144.531
CVP_Trash_Rack_1	2022-03-24 23:29:31	2022-04-05 08:52:42	2022-05-19 06:56:12	33	13.31	144.500
Clifton_Court_Intake_Canal	2022-03-26 09:31:50	2022-04-01 06:42:27	2022-04-06 12:45:47	12	4.84	142.721
Old_River_Quimby	2022-04-03 21:39:12	2022-04-03 21:39:12	2022-04-03 21:39:12	1	0.40	141.000
Benicia_east	2022-04-02 07:20:46	2022-04-11 13:06:51	2022-05-22 14:36:14	13	5.24	52.240
Benicia_west	2022-04-02 07:23:03	2022-04-11 13:23:23	2022-05-22 17:23:11	13	5.24	52.040

4.2 Detections for Head_of_Old_River release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
Old River	2022-03-27 20:52:16	2022-03-31 22:03:43	2022-04-06 19:24:20	6	4.17	153.001
MiddleRiver	2022-03-25 11:17:13	2022-03-30 19:17:23	2022-04-06 07:29:42	4	2.78	150.000
Clifton_Court_US_Radial_Gates	2022-03-20 15:26:55	2022-04-01 17:33:28	2022-06-07 09:29:44	28	19.44	146.000
Holland_Cut_Quimby	2022-03-27 15:13:48	2022-04-14 16:43:35	2022-05-24 16:03:28	7	4.86	145.000
CVP_Tank	2022-03-28 23:39:05	2022-04-02 13:32:56	2022-04-22 13:52:32	8	5.56	144.531
CVP_Trash_Rack_1	2022-03-21 03:31:53	2022-03-31 14:26:07	2022-05-04 11:28:54	31	21.53	144.500
Clifton_Court_Intake_Canal	2022-03-29 08:41:37	2022-04-10 00:08:29	2022-05-12 03:28:04	9	6.25	142.721
Old_River_Quimby	2022-03-31 05:16:51	2022-03-31 05:16:51	2022-03-31 05:16:51	1	0.69	141.000
Benicia_east	2022-03-31 06:21:56	2022-04-15 11:27:51	2022-05-25 16:41:41	13	9.03	52.240
Benicia_west	2022-03-31 06:28:32	2022-04-15 10:17:52	2022-05-25 16:45:27	13	9.03	52.040

4.2 Detections for Stockton release groups

general_location	First_arrival	Mean_arrival	Last_arrival	Fish_count	Percent_arrived	river_km
Old River	2022-03-26 12:58:55	2022-03-30 06:51:55	2022-04-03 00:44:55	2	2	153.001
MiddleRiver	2022-03-26 10:41:41	2022-04-09 04:19:27	2022-05-29 02:38:53	8	8	150.000
Clifton_Court_US_Radial_Gates	2022-03-29 09:17:07	2022-04-01 07:11:58	2022-04-05 10:29:40	4	4	146.000
Holland_Cut_Quimby	2022-03-31 10:37:46	2022-04-02 14:36:02	2022-04-03 17:21:19	4	4	145.000
CVP_Tank	2022-04-07 07:36:54	2022-04-07 07:36:54	2022-04-07 07:36:54	1	1	144.531
CVP_Trash_Rack_1	2022-03-28 12:01:44	2022-04-02 04:06:10	2022-04-05 08:15:04	6	6	144.500
Clifton_Court_Intake_Canal	2022-03-29 23:37:05	2022-03-30 05:52:45	2022-03-30 12:08:26	2	2	142.721
Old_River_Quimby	2022-03-23 01:52:29	2022-03-27 14:04:50	2022-04-01 12:57:07	4	4	141.000
Benicia_east	2022-03-24 12:53:31	2022-04-04 01:11:52	2022-04-08 11:47:16	10	10	52.240
Benicia_west	2022-03-23 22:09:11	2022-04-02 20:40:27	2022-04-08 11:52:35	11	11	52.040

4.3 Fish arrivals per day (“NA” means receivers were non-operational)

try(setwd(paste(file.path(Sys.getenv("USERPROFILE"),"Desktop",fsep="\\"), "\\Real-time data massaging\\products", sep = "")))

## THIS CODE CHUNK WILL NOT WORK IF USING ONLY ERDDAP DATA, REQUIRES ACCESS TO LOCAL FILES
if (nrow(detects_study[is.na(detects_study$DateTime_PST)==F,]) == 0){
  "No detections yet"
} else {
  arrivals <- aggregate(list(DateTime_PST = detects_study$DateTime_PST), by = list(general_location = detects_study$general_location, TagCode = detects_study$TagCode), FUN = min)
    
  beacon_by_day <- fread("beacon_by_day.csv", stringsAsFactors = F)
  beacon_by_day$day <- as.Date(beacon_by_day$day)
  
  gen_locs <- read.csv("realtime_locs.csv", stringsAsFactors = F)
  
  arrivals$day <- as.Date(format(arrivals$DateTime_PST, "%Y-%m-%d", tz = "Etc/GMT+8"))
  
  arrivals_per_day <- aggregate(list(New_arrivals = arrivals$TagCode), by = list(day = arrivals$day, general_location = arrivals$general_location), length)
  arrivals_per_day$day <- as.Date(arrivals_per_day$day)

  ## Now subset to only look at data for the correct beacon for that day
  beacon_by_day <- as.data.frame(beacon_by_day[which(beacon_by_day$TagCode == beacon_by_day$beacon),])
  
  endtime <- min(as.Date(format(Sys.time(), "%Y-%m-%d")), max(as.Date(detects_study$release_time)+(as.numeric(detects_study$tag_life)*1.5)))
  ## Now only keep beacon by day for days since fish were released
  beacon_by_day <- beacon_by_day[beacon_by_day$day >= as.Date(min(study_tagcodes$release_time)) & beacon_by_day$day <= endtime,]  
  
  beacon_by_day <- merge(beacon_by_day, gen_locs[,c("location", "general_location","rkm")], by = "location", all.x = T)

  arrivals_per_day <- merge(unique(beacon_by_day[,c("general_location", "day", "rkm")]), arrivals_per_day, all.x = T, by = c("general_location", "day"))
  
  arrivals_per_day$day <- factor(arrivals_per_day$day)
  
  ## Remove bench test and other NA locations
  arrivals_per_day <- arrivals_per_day[!arrivals_per_day$general_location == "Bench_test",]
  arrivals_per_day <- arrivals_per_day[is.na(arrivals_per_day$general_location) == F,]
  
  ## Change order of data to plot decreasing river_km
  arrivals_per_day <- arrivals_per_day[order(arrivals_per_day$rkm, decreasing = T),]
  arrivals_per_day$general_location <- factor(arrivals_per_day$general_location, unique(arrivals_per_day$general_location))
  
  # 
  # ggplot(data=arrivals_per_day, aes(x=general_location, y=fct_rev(as_factor(day)))) +
  # geom_tile(fill = "lightgray", color = "black") + 
  # geom_text(aes(label=New_arrivals)) +
  # labs(x="General Location", y = "Date") +
  # theme(panel.background = element_blank(), axis.text.x = element_text(angle = 90, hjust = 1))
    crosstab <- xtabs(formula = arrivals_per_day$New_arrivals ~ arrivals_per_day$day + arrivals_per_day$general_location, addNA =T)
  crosstab[is.na(crosstab)] <- ""
  crosstab[crosstab==0] <- NA
  crosstab <- as.data.frame.matrix(crosstab)
  #colnames(crosstab) <- c("Butte Br", "Tower Br", "I8050 Br", "Old River", "Middle River", "CVP Tanks", "Georg Slough1", "Sac_Blw Georg1", "Georg Slough2", "Sac_Blw Georg2", "Benicia East", "Benicia West")

 kable(crosstab, align = "c") %>%
  kable_styling(c("striped", "condensed"), font_size = 11, full_width = F, position = "left") %>%
  #row_spec(0, angle = -45) %>%
  column_spec(column = 1:ncol(crosstab),width_min = "50px",border_left = T, border_right = T) %>%
  column_spec(1, bold = T, width_min = "75px")%>%
  scroll_box(height = "700px")
}

	Blw_Salt_RT	Abv_Otter_Island	ButteBrRT	MeridianBr	TowerBridge	I80-50_Br	Old River	MiddleRiver	Clifton_Court_US_Radial_Gates	Holland_Cut_Quimby	CVP_Tank	CVP_Trash_Rack_1	Clifton_Court_Intake_Canal	Old_River_Quimby	Sac_BlwGeorgiana	Sac_BlwGeorgiana2	Benicia_east	Benicia_west
2022-03-15				NA
2022-03-16				NA
2022-03-17				NA
2022-03-18				NA
2022-03-19				NA
2022-03-20				NA					1
2022-03-21				NA					1			3
2022-03-22				NA					3			1
2022-03-23				NA					1			2		1				1
2022-03-24				NA					2			2					1	1
2022-03-25				NA				1	2			3
2022-03-26				NA			1	1	2			4	1	1
2022-03-27				NA			1	1	6	1		4
2022-03-28				NA			2		7		1	5		1
2022-03-29				NA				1	5		3	10	4
2022-03-30				NA				1	5	1	2	9	6				1	1
2022-03-31				NA			2	1	4	2		3	1	1			2	2
2022-04-01				NA			1		9			2	2	1			2	2
2022-04-02				NA			1	1	2	2	1	3	3				1	1
2022-04-03				NA			1	1	3	2		1		1			3	3
2022-04-04				NA			1		1	1		3	1				2	3
2022-04-05				NA			1	1	3		2	2					5	5
2022-04-06				NA			2	1	1	1		1	3				3	2
2022-04-07				NA							3	1
2022-04-08				NA				1		1							3	3
2022-04-09				NA													4	4
2022-04-10				NA				1									2	2
2022-04-11				NA					1								1	1
2022-04-12				NA								1
2022-04-13				NA
2022-04-14									1
2022-04-15												1
2022-04-16												1					1	1
2022-04-17
2022-04-18
2022-04-19
2022-04-20												2
2022-04-21
2022-04-22									2		1	1
2022-04-23
2022-04-24
2022-04-25									1			1					1	1
2022-04-26
2022-04-27
2022-04-28
2022-04-29
2022-04-30
2022-05-01
2022-05-02																	1	1
2022-05-03
2022-05-04									1			1
2022-05-05
2022-05-06
2022-05-07
2022-05-08
2022-05-09												1
2022-05-10													1
2022-05-11
2022-05-12													1
2022-05-13									1	1		1
2022-05-14
2022-05-15								1	1
2022-05-16
2022-05-17								1										1
2022-05-18																	1
2022-05-19										1		1
2022-05-20
2022-05-21
2022-05-22									1								1	1
2022-05-23
2022-05-24										1
2022-05-25																	1	1
2022-05-26
2022-05-27
2022-05-28
2022-05-29								1
2022-05-30
2022-05-31
2022-06-01
2022-06-02
2022-06-03
2022-06-04
2022-06-05
2022-06-06
2022-06-07		NA							1
2022-06-08		NA
2022-06-09		NA
2022-06-10		NA
2022-06-11		NA
2022-06-12		NA
2022-06-13		NA
2022-06-14		NA
2022-06-15		NA
2022-06-16		NA
2022-06-17	NA	NA
2022-06-18	NA	NA
2022-06-19	NA	NA
2022-06-20	NA	NA
2022-06-21	NA	NA
2022-06-22	NA	NA
2022-06-23	NA	NA
2022-06-24	NA	NA
2022-06-25	NA	NA
2022-06-26	NA	NA
2022-06-27	NA	NA
2022-06-28	NA	NA
2022-06-29	NA	NA
2022-06-30	NA	NA
2022-07-01	NA	NA
2022-07-02	NA	NA
2022-07-03	NA	NA
2022-07-04	NA	NA
2022-07-05	NA	NA
2022-07-06	NA	NA
2022-07-07	NA	NA
2022-07-08	NA	NA
2022-07-09	NA	NA
2022-07-10	NA	NA
2022-07-11	NA	NA
2022-07-12	NA	NA
2022-07-13	NA	NA
2022-07-14	NA	NA

rm(list = ls())
cleanup(ask = F)

For questions or comments, please contact cyril.michel@noaa.gov